Coursework

Policies, recommendations, and reviews regarding courses.

Introduction

Three main considerations when selecting courses:

1. Meeting departmental and program requirements

2. Preparation for the qualifying exam

3. Selecting courses that relate to your research project

Program requirements

View department policy here. In short, you must select a Technical Area in addition to the math focus. Typically students in this lab select Area C, Dynamics and Controls. You must then take:

• A minimum of one course from your technical area

• A minimum of two of the courses listed under the Mathematics Requirement. At least one of these courses has to be a MATH course

• A minimum of three courses from the 500-799 level in the BIOM, CE, CHE, CHEM, CPE, CS, EE, IENG, IH&S, MAE, MATH, MINE, PNGE, PHYS, SAFM, SENG, or STAT subjects

  • 500+ level courses not in these subjects can be approved on a case-by-case basis by the Graduate Committee. That's a whole other can of worms that will be covered in its own section.

• At least 24 hours of Research - MAE 797

GPA and Credit Hour Requirements

GPA Requirements

As per department policy, PhD students are required to maintain "a minimum cumulative GPA of 3.0 in all courses." This is the department-specific wording for the idea that anything under a B is failing in grad school. Fortunately, professors teaching 500+ level courses typically have some manner of more lenient grading (e.g. more relaxed letter grade boundaries) to help compensate for this. No reasonable department wants a student who is willing to put in the work to fail out of grad school; this requirement is to weed out those who should not be in a grad school environment.

Credit Hour Requirements

The university defines a Full-Time graduate student as someone taking:

• at least 9 credit hours in the Fall or Spring

• at least 6 credit hours in the Summer

Going below these numbers during a semester will bump you to some manner of part-time status. The primary problem with this is that being anything other than a full time student will mean paying into Medicare and other taxes, decreasing the amount of your stipend you receive after taxes. This can be a unpleasant shock if you're not expecting it, so ensuring you're taking the correct number of credits is essential for the Spring and Fall. However, in the Summer, the college tuition and fees for 6 credit hours is ~$600 more than the stipend hit from only taking 1 credit hour and being part time, so generally you should take 1 credit hour over the Summer.

Generally it's recommended that you only take around 1-2 classes per semester during the Fall and Spring as a grad student, so it's common practice to "pad out" the rest of your credit hours with research. When you don't need to take classes, such as during the Summer, you can take all research credits.

Preparation for the Qualifying Exam

Past the requirements from the department, there are many courses that can help prepare you for the qualifying exam, as well as conducting research. This is especially crucial for students coming in directly from their undergraduate degree.

Many of the subjects that differentiate grad school from undergrad are math based. Mathematicians are wonderful people who fill an important niche in society. However, graduate level math courses are not always relevant to an engineer's education. For example, I have taken courses that focused on deriving proofs for arcane formulas without showing me how to apply anything. This fits into a mathematician's education, but did not help me achieve my goals. The goal is to find courses that will teach you tools and help you apply them to your research. I've listed a few below, with some notes. I do not intend to maintain a complete list of math courses, because this changes over time and is maintained elsewhere. If you notice that this list is inaccurate or out of date, please correct it.

Math 441 - Applied Linear Algebra

This course helps you use the topics and tools within the field of Linear Algebra, without getting lost in the weeds of the mathematics behind it. The emphasis is solving large systems of equations, performing least-squares model fits, and eigenvalue problems. Taking this course before MATH 521 would be helpful due to learning the concepts first, then implementing them on Matlab.

Math 521 - Numerical Analysis

This course has been taken by several students in the lab who say it is very helpful for learning relevant analysis techniques, including how to implement them in Matlab.

Math 543 - Linear Algebra

Math 560 - Introduction to Dynamical Systems

Math 563 - Mathematical Modeling

Math 564 - Intermediate Differential Equations

One person strongly suggests you do not take this course.

Math 567/568 - Advanced Calculus

Listed as primarily for engineers and scientists, meaning that the focus is application.

Courses that Relate to your Research

Many courses are offered that may be relevant to robotics, motor control, signal processing, and biological modeling. However, the course catalog changes over time, so use this list only as suggestions. Please reach out to professors to find out when they will be offering special topics courses. Furthermore, keep an eye on the course catalog to identify new courses that may be relevant (and then add them here).

Undergraduate Courses

BMEG 340 – Biomechanics (Jessica Allen)

PR: (BMEG 201 or MAE 243) and PHYS 111. Introduction to the basic approach of biomechanics and application in musculoskeletal, bone and human motion mechanics problems. Includes kinematics to analyze human motion, biomechanics of bone and skeletal system and biomechanical behavior of fibers.

BIOL 476 – Computational Neuroscience (Gary Marsat)

PR: BIOL 348 or consent. Tools and concepts used to probe and characterize the dynamics of neurons, neural networks and neural coding mechanisms. Lectures introducing concepts and discussion sessions focusing on current research literature complement computer laboratories where the student learns programming skills, analytical tools and neural modeling methods used in computational neuroscience research.

Graduate Courses

EXPH 583 – Neuromechanics (Sergiy Yakovenko)

Core concepts in Neuromechanics. Fundamental concepts in computational neuroscience and biomechanics with applications to the analyses of movement control. This includes the use of Simulink and Matlab to solve problems in kinematics (matlab), dynamics, passive walkers (opensim), muscle modeling, stretch reflexes, CPGs, numerical simulation, and optimization.

PT 793A – Special Topics: Advanced Concepts in Motor Control (Valeriya Gritsenko)

A study of contemporary topics selected from recent developments in the field.

MAE XXX – Neurorobotics (Nicholas Szczecinski)

Review key concepts in computational neuroscience (nonspiking and spiking models) and mechanical dynamics (Newtonian and Lagrangian dynamics). Introduce the Functional Subnetwork Approach for designing dynamical neural networks for controlling motion.

MAE 565 – Artificial Intelligence Technology in MAE (Mario Perhinschi)

Introduction to solving complex problems in mechanical and aerospace engineering using genetic (evolutionary) algorithms, fuzzy logic-based modeling and control, and artificial neural networks.